Sleep complements wakefulness by supporting an array of cognitive functions. Yet its role in complex behavior such as value-based decision-making remains to be explored. Substantial evidence suggests that the storage of reward-related information does not stay sedentary during sleep. Spontaneous neural activation has been observed in the ventral striatum (Peigneux et al., 2004; Pennartz et al., 2004; Lansink et al., 2008; Lansink et al., 2009) and other brain structures implicated in reward processing (Cantero et al., 2003; Schabus et al., 2007; Singer and Frank, 2009; Fujisawa and Buzsáki, 2011). This engagement of reward circuits has been postulated to promote the consolidation of reward-related information (Pennartz et al., 2004; Fischer and Born, 2009; Pennartz et al., 2011), and prioritize information of high motivational saliency for sleep-dependent reprocessing (Fischer and Born, 2009; Sterpenich et al., 2009; Wilhelm et al., 2011). Some studies also implicate connections of the active reward system in the sleeping brain with abnormal behavior, such as compulsive eating disorders, which have been associated with the dysfunction in reward networks (Perogamvros et al., 2012a). It remains to be explored, however, whether neural activity during sleep directly supports or modulates cognitive processes related to reward processing in ways that affect goal-directed behaviors, despite potential implications of such research for understanding the nature of mechanisms underlying sleep and reward processing and for clinical interventions aiming at modifying human behavior, such as overeating or smoking, to prevent diseases.

A classic model for research in the field of reward processing and value-based behavior entails preference-guided decisions (Rangel et al., 2008). Choosing among different options involves computing a subjective value for each option and comparing these values to make a decision (Samuelson, 1938; Rangel and Hare, 2010). The capacity to effectively evaluate resources and choices is critical for survival and thriving, and the ways of fine-tuning such valuation to reflect subtle changes in the environment or the cognitive state of a decision-maker represent important adaptive systems in animals. Indeed, extant data have demonstrated that subjective preferences are not formed once and forever, but can be modulated by external (e.g. reward reinforcement (Tobler et al., 2005)) and internal factors in the waking state. For example, memory retrieval has been shown to influence the weighting of choice options in certain types of decisions (Barron et al., 2013; Gluth et al., 2015; Bornstein and Norman, 2017), whereas attention affects the comparison of choice values in a range of economic behaviors (Armel et al., 2008; Krajbich et al., 2010; Schonberg et al., 2014). These data thus argue for labile preferences and provide a foundation for investigating the vulnerability of preferences to modulation during sleep.

Here, we explored the extent to which sleep-dependent neurocognitive processing affects the evaluation of intrinsic preferences that guide decisions while awake. In particular, we investigated whether preferences for choice options can be specifically targeted and modified during midday nap, using targeted memory reactivation (TMR), a procedure that has been applied to manipulate neurocognitive processing via stimulating the sleeping brain with unobtrusive cues. The method has been shown effective in selectively improving post-sleep performances in a range of cognitive tasks, including spatial memory (Rasch et al., 2007; Rudoy et al., 2009; Diekelmann et al., 2011), motor memory (Antony et al., 2012), vocabulary learning (Schreiner and Rasch, 2015; Schreiner and Rasch, 2017; Tamminen et al., 2017), fear extinction (Hauner et al., 2013; He et al., 2015), and the reduction of implicit social biases (Hu et al., 2015), but its influence on value-based decisions has yet to be explored.

Specifically, we investigated the effect of covert, reward-related stimulation to the sleeping brain on preferences and choices in an important class of behavior characterized by simple economic choices (e.g. choosing between apples and oranges). Such behavior is prevalent in human and non-human animals and has served as a major building block for understanding complex goal-directed behaviors (Fehr and Rangel, 2011; Padoa-Schioppa, 2011). Focusing on simple choices also allows us to exploit existing knowledge regarding cognitive mechanisms associated with decision-making, and in particular the known relationship between choice and reaction time and the distinct latent aspects of the decision process characterized by computational frameworks such as sequential sampling (Smith and Ratcliff, 2004; Gold and Shadlen, 2007). This framework thus allows us to explore how stimulating the sleeping brain affects the underlying cognitive processes that give rise to preferences and decisions.

We first examined whether TMR modifies behavior and whether such modification is sleep-dependent. We implemented TMR through presenting the spoken name of familiar snack items repeatedly and unobtrusively during non-rapid-eye-movement (NREM) sleep and found a significant post-sleep preference enhancement for items that had been cued relative to items not externally cued. In stark contrast, we found no significant differences in preferences and choices in a control group who underwent the same treatment but were exposed to verbal stimuli while awake. We then examined the extent to which neurocognitive processing during sleep contributes to post-sleep preference shift. By monitoring brain activity during sleep using electroencephalographic recordings and relating this neural measure to behavioral evidence of preference shifts, we found that cueing-specific preference improvements can be reliably predicted by delta or theta band signals induced by cues during sleep. Finally, we fit the decision and reaction time data observed in simple choices using a well-established sequential sampling model to explore the influence of verbal cueing during sleep on the subsequent decision process.

We tested 92 healthy adults (Materials and methods and Supplementary file 1, Table 1A for demographic information) in a battery of classic simple economic choice experiments in combination with the TMR procedure (Figure 1). Subjects were first introduced to a set of 60 familiar snack items (self-reported familiarity rating = 3.51 ± 0.84, on a 5-point scale, Supplementary file 1, Table 1B), and indicated their willingness-to-pay (WTP) in a Becker-DeGroot-Marschak (BDM) auction (Becker et al., 1964), which served as a measure of the baseline preference for a snack item (henceforth WTP1) (Plassmann et al., 2007; Hare et al., 2008; Schonberg et al., 2014). To establish an association between snacks and verbal cues, the spoken name of each snack (e.g. ‘M and M’s candies’) was delivered over a speaker along with the presentation of the item image on a computer screen during the BDM auction. On the basis of WTP1, we chose eight pairs of snacks for each subject that were matched by baseline preferences and randomly divided into cued and uncued conditions.

Figure 1

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During subsequent sleep (sleep group, N = 47), the names of eight snack items (i.e. cued items) were delivered over a speaker, each for ten repetitions, during the stage 2 of non-rapid eye movement (N2) sleep with no sleep disruption (Schreiner and Rasch, 2015; Cairney et al., 2018). After waking, we probed the effect of verbal cueing on preferences by re-evaluating the WTP for all 60 items using the same BDM auction (henceforth WTP2). We also probed changes in choices by performing a binary decision task where subjects needed to select between a pair of snacks that were matched for the baseline WTP but differed by whether they had or had not been named during sleep. As a control, we also measured behavior in a control group where participants underwent the same experimental procedure but, rather than taking a 90 min nap as in the asleep condition, were kept awake for the same duration of time (wake group, N = 45; Materials and methods and Supplementary file 1, Table 1A). Sleep monitoring and staging were performed using standard criteria from polysomnography recordings (Methods).

Verbal cueing during sleep but not wakefulness improves preferences for previously cued items

First, we investigated the effect of verbal cueing on the subjective value of a snack by comparing the paired difference of WTP elicited from the first and second BDM auction (i.e. WTP2-WTP1), which for simplicity we refer to as ‘ΔWTP’. Consistent with previous evidence that WTP reliably reflects the subjective value of a familiar option (Plassmann et al., 2007), the baseline ΔWTP (i.e. ΔWTP for items that had not been cued) was not significantly different from zero in either subject group (all subjects, ΔWTP = 0.11 ± 0.12, t₉₁ = 0.99, p = 0.323; sleep vs. wake, t₉₀ = - 0.97, p = 0.336), suggesting the test-and-retest stability of preferences for snack items examined in our experiment.

Using ΔWTP, we found that there was no significant main effect in ΔWTP between the sleep and wake groups (sleep: 0.29 ± 0.12; wake: 0.32 ± 0.11; main effect in cohort, F_1,90 = 0.02, p = 0.886; Figure 2A). On the other hand, there was a significant within-subject difference associated with cueing, such that previously cued items were associated with higher ΔWTP relative to uncued items (cued: 0.50 ± 0.11; uncued: 0.11 ± 0.12; main effect in cueing condition, F_1,90 = 27.61, p = 9.9 × 10⁻⁷; Figure 2A). Importantly, cue-induced effects on preferences varied between sleep and wake cohorts as predicted: ΔWTP was significantly higher for cued relative to uncued items in the sleep group but not in the wake group ((cued vs. uncued) × (sleep vs. wake): F_1,90 = 6.95, p = 0.01; Figure 2A), suggesting that verbal cueing during sleep but not wakefulness promotes preferences for previously cued items. This interaction was significant when analyzed using permutation tests that do not require specific distributional assumptions (Figure 2—figure supplement 1). The result was also robust after taking into account of filler items (Figure 2—figure supplement 2), demographic variables, and changes in self-reported measures of hunger and vigilance before and after the verbal cueing session (Supplementary file 1, Table 1C).

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Verbal cueing during sleep does not influence choice randomness at either the subject or item level

To evaluate the possibility that, rather than preference shifts, the observed choice difference between the sleep and wake groups could be attributed to alternative factors such as changes in choice randomness, we compared the extent to which decisions were guided by value differences between the two participant groups. Specifically, we regressed the likelihood that subjects chose cued over uncued items against the difference in WTP2 (i.e. WTP2_cued -WTP2_uncued; Figure 3A). Consistent with previous studies (Sugrue et al., 2005), WTP2 differences predicted the choice behavior in both sleep and wake groups (sleep: r = 0.37, p = 0.011; wake: r = 0.39, p = 0.009), whereas WTP1 differences had no predictive power for the choice behavior in either subject group (sleep: r = 0.01, p = 0.945; wake: r = 0.05, p = 0.746). Importantly, if changes in choice randomness contribute to the observed behavioral bias under the sleep protocol, we would expect a steeper regression slope in sleep group participants relative to that of the wake group. In contrast to this prediction, the regression results showed no significant differences in slopes between the sleep and wake cohorts (sleep = 0.06; wake = 0.08; F_1,88 = 0.19, p = 0.665). Differences were found only in the intercepts (sleep = 0.56, wake = 0.51, F_1,88 = 6.34, p = 0.014; Figure 3A), suggesting that the behavioral differences between sleep and wake protocols could not be attributed to differential choice randomness but rather to an overall shift in choice propensity toward cued items under the sleep protocol. Similar results were obtained when examining the relationship between choices and WTP biases at the item level (Figure 3B).

Figure 3

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Cue-induced delta and theta band power predicts a post-sleep preference shift at both the subject and item level

Next, we investigated how the sleeping brain processed the spoken name of a snack and how such processing contributed to post-sleep behavior using event-related potential (ERP) data measured in a subset of sleep group participants (N = 23; Supplementary file 1, Table 1D, Table 1E; Materials and methods). By averaging ERP amplitudes measured over the interval from −200 to 1800 ms with respect to the onset of each verbal cue, we observed a cue-evoked neural response at the frontal electrode sites (representative electrode F3) during N2 sleep (Figure 4A). Characterized by a positive ERP around 450 ms followed by a negative peak around 1000 ms, this observed pattern is similar to the well-established waveform of K-complexes (KCs) (Figure 4—figure supplement 1A and Supplementary file 1, Table 1F) (Niiyama et al., 1995; Cote et al., 1999; Blume et al., 2017), an electroencephalogram (EEG) graphoelement that has been repeatedly associated with the processing of sensory stimuli, including auditory cues, mainly during periods identified as N2 (Halász, 2005; Cash et al., 2009).

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We then examined how verbal cueing evoked power changes at different frequency bands by analyzing event-related changes in spectral power (ERSPs) before and after the cue onset. For each stimulus presentation, we segmented trials from −800 to 1800 ms with respect to the cue onset (Ruch et al., 2014; Schreiner and Rasch, 2015). Using the mean spectral power measured over the interval from −800 to −200 ms as a baseline, we computed the event-related change relative to the baseline from 0 to 1800 ms (Schreiner and Rasch, 2015). We observed no significant increase in high-frequency activity (8–35 Hz), which would have been observed if verbal stimuli had reduced the depth of sleep in participants (Figure 4A) (Ruch et al., 2014). More specifically, by contrasting the mean spectral power for high-frequency bands before and after the stimulus presentation, we found no significant change for the alpha, beta, or gamma bands (alpha: t₂₂ = - 1.51, p = 0.728; beta: t₂₂ = - 1.68, p = 0.535; gamma: t₂₂ = - 0.23, p = 1; all Bonferroni corrected). In sharp contrast, presentation of verbal stimuli elicited strong event-related synchronization for low-frequency activity (0.5–8 Hz), including the delta band (t₂₂ = - 6.88, p = 3.3 × 10⁻⁶) (Figure 4—figure supplement 1B), which has been previously implicated in the detection of motivationally salient stimuli (Blume et al., 2017) and improved memory consolidation (Oudiette et al., 2013; Batterink et al., 2016), and the theta band (t₂₂ = - 7.13, p = 1.9 × 10⁻⁶), which has been associated with memory processing during wakefulness (Lisman and Jensen, 2013) and successful cueing during non-rapid eye movement sleep in both healthy (Schreiner and Rasch, 2015) and patient (Hot et al., 2011) populations.

Importantly, the cue-induced change in power of the delta and theta bands strongly predicted the degree of cueing-specific preference enhancement after waking, at both the individual and item level. That is, among the sleep group subjects, individuals associated with more prominent increases in average delta or theta band power later demonstrated more pronounced post-sleep preference enhancements for cued items (delta: r = 0.65, p = 0.005; theta: r = 0.64, p = 0.004; all Bonferroni corrected; Figure 4B–C). Similarly, among items that had been presented during sleep, those associated with more prominent increases in average delta or theta band power showed greater post-sleep improvement in WTP (delta: r = 0.52, p = 0.001; theta: r = 0.47, p = 0.005; all Bonferroni corrected; Figure 4D–E). In contrast, there was no correlation between the post-sleep change in preference for cued items and the changes in power in the alpha, beta, or gamma bands (alpha: p = 0.463; beta: p = 1; gamma: p = 1; Bonferroni corrected; Supplementary file 1, Table 1G). Similarly, there was no correlation between the preference change for uncued items and the power change in either the high or low-frequency bands at either the subject or item level (alpha: p = 0.229; beta: p = 0.877; gamma: p = 1; Bonferroni corrected). These results suggest that preference modification following sleep TMR results from neurocognitive processing of verbal stimuli during sleep, as indexed by the power of low-frequency bands evoked by verbal cues.

Combining TMR with classic economic paradigms, the present study demonstrates an intriguing connection between sleep and subjective preferences in an important class of decisions widely used for investigating goal-directed behavior. In particular, the study provides behavioral, neural, and modeling evidence indicating that covert cueing during midday nap can alter preferences and choices in a selective manner, independent of more general effects on decision-making such as hunger, vigilance, or choice randomness. The fact that the stimulation effect is sleep-dependent and that cueing in the waking state produces no significant changes in preferences or choices is consistent with past evidence that selective memory reactivation during wakefulness does not always improve memory (Rudoy et al., 2009; Antony et al., 2012; Schreiner and Rasch, 2015) and sometimes even produces opposing consequences (Diekelmann et al., 2011). Importantly, and consistent with previous studies (Antony et al., 2012; Oudiette et al., 2013; Schreiner and Rasch, 2015), the degree of post-sleep preference enhancement can be predicted by cue-induced increases of theta or delta power, which have been previously associated with successful cueing during sleep (Schreiner et al., 2015; Schreiner and Rasch, 2015) and with improved memory consolidation (Oudiette et al., 2013; Batterink et al., 2016), respectively.

Results of the current study provide novel insights into the nature of cognitive processes that support value evaluation. Our findings corroborate a wealth of evidence suggesting that subjective preferences interact dynamically with the environmental and cognitive states of a decision-maker, and extend these previous findings by demonstrating that sleep also contributes to the dynamic nature of preferences in a flexible, selective manner, such that preferences for specific options can be individually biased during sleep.

In particular, our results are consistent with two broad accounts previously proposed for how preferences could be dynamically modulated. The first involves the possibility that preferences are informed by memory, such that weighting a choice option entails retrieving relevant information from memory at the time of decision-making (Pennartz et al., 2011; Bornstein et al., 2017). Under this interpretation, preference reflects the outcome of memory retrieval, and verbal cueing during sleep encourages the preference of a familiar, valued item by reactivating and strengthening the relevant memories. For example, sleep TMR may trigger the reactivation of reward-related information associated with the cued snack or assist the consolidation of past episodes, such as purchasing or consuming the cued item. In keeping with this possibility, previous studies have suggested that important memories, including those of motivational salience, are preferentially reactivated during sleep (Lansink et al., 2008; Dunsmoor et al., 2015; Blume et al., 2017) and that successful retrieval of a relevant episodic memory (e.g. a past action (Bornstein et al., 2017) or contextual information associated with an action (Bornstein and Norman, 2017)) leads to subsequent decision biases. Neurally, this possibility is consistent with evidence supporting the interplay between memory and choice processes, which involves a network of brain regions, including the hippocampal region, in the processing of memory-related value signals (Barron et al., 2013; Gluth et al., 2015; Palombo et al., 2015; Weilbächer and Gluth, 2016).

At the computational level, this interpretation is consistent with the proposal that connects preference-guided choices and mnemonic processes under the framework of sequential sampling (Gluth et al., 2015; Shadlen and Shohamy, 2016). Interestingly, using LBA, a variant of sequential sampling model, we found that sleep TMR selectively accelerated the accumulation of evidence supporting cued items as measured by drift rate. Past theoretical and empirical work has suggested that the drift rate in sequential sampling models reflects the quality of information drawn from stimuli during the evidence accumulation (Ratcliff and McKoon, 2008; Ratcliff et al., 2016). Within the domain of memory, a higher drift rate is associated with recognizing an item that has been previously presented multiple times relative to an item presented only once (Ratcliff and McKoon, 2008; Gluth et al., 2015). The correlation between drift rate biases and WTP biases observed in our data thus indicates the intriguing possibility that memory that is selectively reactivated during sleep renders higher quality evidence signals in favor of the cued item, giving rise to behavioral shift toward items previously named during sleep. Future work employing various process models (Forstmann et al., 2016) will be needed to further connect behavioral shifts with measures of memory change before and after TMR to investigate which memory is reactivated (e.g. episodic, semantic, emotional) and how different types of memories differentially affect the choice process.

A second but not mutually exclusive possibility is that rather than playing a direct role in affecting value computation and comparison, verbal cueing initiates a chain of events that exerts modulatory effects on the decision process. For example, the behavioral shift observed in our data may reflect the modulatory effect of memory on attention, which in turn biases preferences and choices at the time of decision-making. In line with this hypothesis, recent evidence based on either eye fixation (Krajbich et al., 2010) or cue-approach training (Schonberg et al., 2014; Bakkour et al., 2017) has suggested the influence of attention on decisions. Consequently, if individuals pay more attention to better-remembered options, preference enhancement following selective memory reactivation during sleep may reflect an increase in attention due to improved memory consolidation (Chun and Turk-Browne, 2007; Goldfarb et al., 2016; Rosen et al., 2016). Under this hypothesis, the drift rate identified in the computational model may be associated with attention bias between cued and uncued items, and possibly scaled with the degree to which attention is affected by memory after verbal cueing. Future work incorporating behavioral probes for changes in attention before and after verbal cueing is needed to test and discriminate between these possibilities. Additional experiments may also include neuroimaging studies to identify neural correlates and functional coupling in brain regions previously implicated in attention, memory, and reward processing.

The results of the present study suggest that sleep likely represents a unique period during which preferences and choices that are otherwise stable can be selectively modified by external cues. This finding is consistent with past evidence that targeted memory reactivation not only benefits memories of newly acquired information but can also be used to eliminate long-standing social biases (Hu et al., 2015). It remains to be elucidated whether the findings of the present study are specific to N2 or are generalizable to other stages of sleep, in particular, slow-wave sleep, the primary focus of previous TMR studies. On the one hand, there is evidence for the capacity of neurocognitive processing of verbal stimuli during N2, including extracting information from incoming words (Kouider et al., 2014), differentiating their motivational saliency (Blume et al., 2017), and reactivating the existing motor or vocabulary memories associated with the words (Kouider et al., 2014; Schreiner and Rasch, 2015). Accordingly, one might expect that N2 may be particularly suitable for implementing TMR using verbal stimuli, similar to those employed in our study. On the other hand, it is also possible that our results are not restricted to the period of light sleep, given the wealth of evidence on TMR during slow-wave sleep in humans (Rasch et al., 2007; Rudoy et al., 2009; Antony et al., 2012) and the entire non-rapid eye movement sleep in rodents (Rolls et al., 2013; Barnes and Wilson, 2014). Future studies examining night sleep as opposed to daytime naps or involving deprivation of particular sleep stages are needed to establish the specificity of the observed effects for different sleep stages.

Our results also raise intriguing questions regarding whether the preference enhancement is specific to cued memory reactivation, or hold more generally for both cued and spontaneous reactivation during sleep. According to the active system consolidation model, sleep-dependent memory consolidation critically relies upon repeated reactivation of memory representations (Diekelmann and Born, 2010; Rasch and Born, 2013). It is therefore possible that the amount of memory reactivation taking place during sleep modulates preference improvement. TMR likely plays a role of triggering and enhancing such reactivation, thereby magnifying the influence on preference in the sleeping brain. Under this hypothesis, memory reactivation, either cued or spontaneous, contributes to the fine-tuning of reward evaluation. Yet unlike cued reactivation, which is predicted to bias the weighting of specific choice option likely through its selective influence on memory traces, spontaneous reactivation may exert a less discriminative impact on valuation. For example, there is evidence that, following overnight sleep, subjects demonstrate an overall increase in favorable perception of the whole choice set, due to enhanced recalls for positive information associated with choice options (Karmarkar et al., 2017). In the present study, however, midday nap produces no general effect on either the overt behavior or computational parameters reflecting the latent cognitive processing directly related to decision. One possibility is that the beneficial effect of sleep on memory consolidation requires sufficient sleep duration and the presence of slow-wave sleep (Backhaus and Junghanns, 2006; Diekelmann et al., 2011; Diekelmann et al., 2012), whereas daytime naps usually consist of lighter sleep stages such as stage 1 and 2 (Backhaus and Junghanns, 2006; Tucker et al., 2006; Genzel et al., 2014). TMR during nap in our experiment may serve to elicit memory reactivation that typically occurs during a longer sleep period. Consistent with this interpretation, previous research shows similar effects of memory stabilization following either 40-min sleep with TMR or 90-min sleep without external stimulation (Diekelmann et al., 2012). Future investigations using night sleep or animal models will be needed to assess whether and how spontaneous memory reactivation affects the storage of reward-related information and to determine whether sleep plays a general role in modulating goal-directed behavior through tuning reward evaluation.

More broadly, simple choices are fundamental for goal-directed behavior and have served as a cornerstone for studying reward and motivational control across species (Fehr and Rangel, 2011; Padoa-Schioppa, 2011). Results of this study provide novel insights into the link between sleep and preferences elicited by simple choices, thereby pointing to the possibility of shaping a range of behavior through nudging human sleep. Our results, together with previous findings on sleep-dependent reward-related neural activation (Pennartz et al., 2004; Lansink et al., 2008; Lansink et al., 2009; Perogamvros and Schwartz, 2012b; Igloi et al., 2015), raise the intriguing question regarding whether, and under what circumstances, value assessment and comparison can be perturbed by external cues while asleep. Future studies are needed to address whether our findings generalize to other types of preferences, including those involving risk and ambiguity (Hsu et al., 2005), temporal discounting (McClure et al., 2004), loss aversion (Tom et al., 2007), and prosocial considerations (Hein et al., 2016).

Data and code used for data analysis are publicly available online via Open Science Framework (OSF) at (https://osf.io/9ndhy/).

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Author details

1. Sizhi Ai

   1. National Institute on Drug Dependence, Peking University, Beijing, China
   2. Department of Cardiology, Heart Center, Henan Key Laboratory of Neurorestoratology, The First Affiliated Hospital of Xinxiang Medical University, Weihui, China

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Writing—original draft, Writing—review and editing

   Contributed equally with

   Yunlu Yin

   Competing interests

   No competing interests declared

2. Yunlu Yin

   1. School of Psychological and Cognitive Sciences, Peking University, Beijing, China
   2. Faculty of Business and Economics, The University of Hong Kong, Hong Kong SAR, China

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Sizhi Ai

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6433-3870

3. Yu Chen

   National Institute on Drug Dependence, Peking University, Beijing, China

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

4. Cong Wang

   1. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
   2. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
   3. IDG/McGovern Institute for Brain Research, Peking University, Beijing, China

   Contribution

   Software, Formal analysis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7997-4200

5. Yan Sun

   National Institute on Drug Dependence, Peking University, Beijing, China

   Contribution

   Conceptualization, Methodology

   Competing interests

   No competing interests declared

6. Xiangdong Tang

   Sleep Medicine Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China

   Contribution

   Validation, Writing—review and editing

   Competing interests

   No competing interests declared

7. Lin Lu

   1. National Institute on Drug Dependence, Peking University, Beijing, China
   2. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
   3. IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
   4. Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth Hospital, Peking University, Beijing, China

   Contribution

   Resources, Validation, Methodology

   Competing interests

   No competing interests declared

8. Lusha Zhu

   1. School of Psychological and Cognitive Sciences, Peking University, Beijing, China
   2. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
   3. IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
   4. Key Laboratory of Machine Perception, Ministry of Education; Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China

   Contribution

   Conceptualization, Formal analysis, Supervision, Validation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   For correspondence

   lushazhu@pku.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8717-6356

9. Jie Shi

   1. National Institute on Drug Dependence, Peking University, Beijing, China
   2. Beijing Key Laboratory on Drug Dependence Research, Beijing, China
   3. The State Key Laboratory of Natural and Biomimetic Drugs, Beijing, China
   4. The Key Laboratory for Neuroscience of the Ministry of Education and Health, Peking University, Beijing, China

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Methodology, Project administration, Writing—review and editing

   For correspondence

   shijie@bjmu.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6567-8160

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